International Journal of Computer Assisted Radiology and Surgery最新文献

{"title":"Quality control system for patient positioning and filling in meta-information for chest X-ray examinations.","authors":"A A Borisov, S S Semenov, Yu S Kirpichev, K M Arzamasov, O V Omelyanskaya, A V Vladzymyrskyy, Yu A Vasilev","doi":"10.1007/s11548-025-03468-0","DOIUrl":"10.1007/s11548-025-03468-0","url":null,"abstract":"<p><strong>Purpose: </strong>During radiography, irregularities occur, leading to decrease in the diagnostic value of the images obtained. The purpose of this work was to develop a system for automated quality assurance of patient positioning in chest radiographs, with detection of suboptimal contrast, brightness, and metadata errors.</p><p><strong>Methods: </strong>The quality assurance system was trained and tested using more than 69,000 X-rays of the chest and other anatomical areas from the Unified Radiological Information Service (URIS) and several open datasets. Our dataset included studies regardless of a patient's gender and race, while the sole exclusion criterion being age below 18 years. A training dataset of radiographs labeled by expert radiologists was used to train an ensemble of modified deep convolutional neural networks architectures ResNet152V2 and VGG19 to identify various quality deficiencies. Model performance was accessed using area under the receiver operating characteristic curve (ROC-AUC), precision, recall, F1-score, and accuracy metrics.</p><p><strong>Results: </strong>Seven neural network models were trained to classify radiographs by the following quality deficiencies: failure to capture the target anatomic region, chest rotation, suboptimal brightness, incorrect anatomical area, projection errors, and improper photometric interpretation. All metrics for each model exceed 95%, indicating high predictive value. All models were combined into a unified system for evaluating radiograph quality. The processing time per image is approximately 3 s.</p><p><strong>Conclusion: </strong>The system supports multiple use cases: integration into an automated radiographic workstations, external quality assurance system for radiology departments, acquisition quality audits for municipal health systems, and routing of studies to diagnostic AI models.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"1829-1833"},"PeriodicalIF":2.3,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144327674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vibroacoustic signatures: proof of concept for simple material characterization during needle interventions.","authors":"K Steeg, W Serwatka, D Rzepka, H Oran, O B Özdil, K Heryan, G A Krombach, M H Friebe","doi":"10.1007/s11548-025-03492-0","DOIUrl":"10.1007/s11548-025-03492-0","url":null,"abstract":"<p><strong>Purpose: </strong>This proof of concept investigates the potential of vibroacoustic signals, originating from a needle tip during puncturing, as a method to differentiate ex vitro materials based on their structural characteristics. The main research question is whether the number and distribution of amplitude events in vibroacoustic waveforms correlates with the material structure, offering a feasible approach for a future real-time tissue differentiation in minimally invasive procedures.</p><p><strong>Methods: </strong>Two types of synthetic foams with different air pocket densities were punctured using a standard Quincke lumbar needle with cutting bevel. Vibroacoustic signals were recorded during the puncture, and the number of amplitude events detected per unit distance was analyzed. The structural differences of the foams were quantified by counting the number of air pockets per unit length. Part of the study was to also consider the impact of puncture / insertion speed on the signal characteristics.</p><p><strong>Results: </strong>A significant correlation was observed between the air pocket density of the foams and the number of detected events per unit distance. The foam with a higher air pocket density produced more detected events compared to the one with a lower density. Insertion speed of the needle did not significantly impact the number of detected events.</p><p><strong>Conclusion: </strong>The findings demonstrate that vibroacoustic signals hold information that allows the differentiation of materials based on their structural properties, laying the foundation for further research into their application in real-time tissue differentiation. Integrating vibroacoustic sensing into minimally invasive procedures could provide valuable additional information about tissue composition and integrity, potentially improving surgical precision in procedures such as tumor biopsies. Further research is needed to validate these findings with biological tissues and refine the technology for clinical use.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"1807-1815"},"PeriodicalIF":2.3,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12476320/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144785944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Meta-UNet: enhancing skin-lesion segmentation with multimodal feature integration and uncertainty estimation.","authors":"O K Sikha, Alaysia Leilani B Stone, Miguel A González Ballester","doi":"10.1007/s11548-025-03490-2","DOIUrl":"10.1007/s11548-025-03490-2","url":null,"abstract":"<p><strong>Purpose: </strong>Medical image segmentation plays a crucial role in diagnostic pipelines. This study investigates the integration of lesion-specific metadata with image data to enhance segmentation accuracy and reduce predictive uncertainty.</p><p><strong>Methods: </strong>The standard U-Net architecture was modified to incorporate lesion-specific metadata (Meta-UNet). Various integration strategies, including addition, weighted addition, and embedding layers, were evaluated. Additionally, a Bayesian Meta-UNet with Monte Carlo Dropout (MCD) was developed to assess the impact of metadata integration on model uncertainty. Uncertainty was quantified using measures such as Confidence Maps, Entropy, Mutual Information, and Expected Pairwise Kullback-Leibler divergence (EPKL). An aggregation strategy was also introduced to provide a single comprehensive uncertainty score per image.</p><p><strong>Results: </strong>Meta-UNet outperformed standard U-Net across PH2, ISIC 2018, and HAM10000 datasets. On PH2, it achieved 84.64% accuracy and 90.62% Intersection over Union (IoU), compared to 83.36% and 89.19%. On ISIC 2018, U-Net scored 71.02 ± 6.69 IoU and 79.89 ± 5.09 Dice. On HAM10000, Meta-UNet achieved 88.66 ± 6.09 IoU and 93.42 ± 5.19 Dice. Meta-UNet reduced uncertainty (e.g., 0.149 vs. 0.1745), highlighting the benefit of metadata integration in improving segmentation accuracy and model confidence.</p><p><strong>Conclusion: </strong>Integrating lesion-specific metadata into the U-Net architecture significantly improves segmentation accuracy and reduces predictive uncertainty. The inclusion of metadata enhances model confidence and reliability, underscoring its potential to strengthen diagnostic segmentation pipelines.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"1911-1922"},"PeriodicalIF":2.3,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12476315/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144745918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radiological data processing system: lifecycle management and annotation.","authors":"Tatiana Bobrovskaya, Yuriy Vasilev, Anton Vladzymyrskyy, Olga Omelyanskaya, Pavel Kosov, Evgeniya Krylova, Artem Ponomarenko, Tikhon Burtsev, Ekaterina Savkina, Maria Kodenko, Svyatoslav Kasimov, Konstantin Medvedev, Anna Kovalchuk, Viktoria Zinchenko, Denis Rumyantsev, Veronika Kazarinova, Serafim Semenov, Kirill Arzamasov","doi":"10.1007/s11548-025-03430-0","DOIUrl":"10.1007/s11548-025-03430-0","url":null,"abstract":"<p><strong>Objective: </strong>To develop a platform for automated processing of radiological datasets that operates independently of medical information systems. The platform maintains datasets throughout their lifecycle, from data retrieval to annotation and presentation.</p><p><strong>Methods: </strong>The platform employs a modular structure in which modules can operate independently or in conjunction. Each module sequentially processes output from the preceding module. The platform incorporates a local database containing textual study protocols, a radiology information system (RIS), and storage for labeled studies and reports.</p><p><strong>Results: </strong>A platform equipped with local permanent and temporary file storages facilitates radiological datasets processing. The platform's modules enable data search, extraction, anonymization, annotation, generation of annotated files, and standardized documentation of datasets.</p><p><strong>Conclusion: </strong>The platform provides a comprehensive workflow for radiological dataset management and is currently operational at the Center for Diagnostics and Telemedicine. Future development will focus on expanding platform functionality.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"1965-1974"},"PeriodicalIF":2.3,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144334428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyzing pediatric forearm X-rays for fracture analysis using machine learning.","authors":"Van Lam, Abhijeet Parida, Sarah Dance, Sean Tabaie, Kevin Cleary, Syed Muhammad Anwar","doi":"10.1007/s11548-025-03485-z","DOIUrl":"10.1007/s11548-025-03485-z","url":null,"abstract":"<p><strong>Purpose: </strong>Forearm fractures constitute a significant proportion of emergency department presentations in pediatric population. The treatment goal is to restore length and alignment between the distal and proximal bone fragments. While immobilization through splinting or casting is enough for non-displaced and minimally displaced fractures. However, moderately or severely displaced fractures often require reduction for realignment. However, appropriate treatment in current practices has challenges due to the lack of resources required for specialized pediatric care leading to delayed and unnecessary transfers between medical centers, which potentially create treatment complications and burdens. The purpose of this study is to build a machine learning model for analyzing forearm fractures to assist clinical centers that lack surgical expertise in pediatric orthopedics.</p><p><strong>Methods: </strong>X-ray scans from 1250 children were curated, preprocessed, and manually annotated at our clinical center. Several machine learning models were fine-tuned using a pretraining strategy leveraging self-supervised learning model with vision transformer backbone. We further employed strategies to identify the most important region related to fractures within the forearm X-ray. The model performance was evaluated with and without region of interest (ROI) detection to find an optimal model for forearm fracture analyses.</p><p><strong>Results: </strong>Our proposed strategy leverages self-supervised pretraining (without labels) followed by supervised fine-tuning (with labels). The fine-tuned model using regions cropped with ROI identification resulted in the highest classification performance with a true-positive rate (TPR) of 0.79, true-negative rate (TNR) of 0.74, AUROC of 0.81, and AUPR of 0.86 when evaluated on the testing data.</p><p><strong>Conclusion: </strong>The results showed the feasibility of using machine learning models in predicting the appropriate treatment for forearm fractures in pediatric cases. With further improvement, the algorithm could potentially be used as a tool to assist non-specialized orthopedic providers in diagnosing and providing treatment.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"1845-1850"},"PeriodicalIF":2.3,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12476382/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144700247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing YOLO for laparoscopic tool detection: novel data augmentation and structural modifications addressing mis-detection of bifurcated targets.","authors":"Yuzhang Liu, Yuichiro Hayashi, Masahiro Oda, Kensaku Mori","doi":"10.1007/s11548-025-03352-x","DOIUrl":"10.1007/s11548-025-03352-x","url":null,"abstract":"<p><strong>Purpose: </strong>Laparoscopic tool detection is vital for assistance of minimally invasive surgeries, aiding tasks like tool pose estimation and surgical navigation. This study enhances YOLO models for better detection of bifurcated targets (BT) in such procedures, addressing the issue of mis-detection of bifurcated targets (MDBT) where BT tips are misidentified as separate entities or overlooked.</p><p><strong>Methods: </strong>We proposed a data augmentation strategy, Random Target Masking, to prevent the model from identifying BT tips as separate laparoscopic tools. Mixup Plus was developed to balance instance count across categories with varying BT proportions. Additionally, we employed the Space-to-Depth Convolution block for downsampling to curb the model's tendency to overlook small-sized BT tips.</p><p><strong>Results: </strong>The YOLOv8 model featuring our modifications, tested on our dataset derived from EndoVis17 and EndoVis18, showed improvement in both <math><msub><mi>mAP</mi> <mn>50</mn></msub> </math> and <math><msub><mi>mAP</mi> <mrow><mn>50</mn> <mo>:</mo> <mn>95</mn></mrow> </msub> </math> metrics on the test dataset. On the BT test dataset specifically, <math><msub><mi>mAP</mi> <mn>50</mn></msub> </math> and <math><msub><mi>mAP</mi> <mrow><mn>50</mn> <mo>:</mo> <mn>95</mn></mrow> </msub> </math> metrics improved by nearly 0.2 and 0.1, respectively. For the Clip Applier category, which has the fewest instances (fewer than 100 instances in the entire training and test dataset), the YOLOv8n model incorporating our proposed modifications increased <math><msub><mi>AP</mi> <mn>50</mn></msub> </math> from 0.0251 to 0.457.</p><p><strong>Conclusion: </strong>This study focused on improving BT detection accuracy in laparoscopic tool detection using YOLO models, incorporating RTM and MUP data augmentation techniques along with SPD-Conv block integration. Experimental evaluations based on the EndoVis datasets validated the enhancements. The ablation study confirmed the effectiveness of each proposed improvement, particularly highlighting the distinct advantages of the proposed data augmentation methods.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"1899-1910"},"PeriodicalIF":2.3,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144555598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Carotid atherosclerotic lesion analysis in 3D based on distance encoding in mesh representation.","authors":"Hinrich Rahlfs, Markus Hüllebrand, Sebastian Schmitter, Christoph Strecker, Andreas Harloff, Anja Hennemuth","doi":"10.1007/s11548-025-03464-4","DOIUrl":"10.1007/s11548-025-03464-4","url":null,"abstract":"<p><strong>Purpose: </strong>The purpose of this study is to support stroke risk analysis, evaluation of therapy effectiveness, and lesion progression through a comprehensive assessment of carotid atherosclerotic lesions in 3D based on automatic segmentation and visualization of quantitative parameters.</p><p><strong>Methods: </strong>We propose a novel method for extracting the pathologically thickened regions from 3D vessel wall segmentations using distance encoding on the inner and outer wall mesh to enable atherosclerotic lesion analysis. A case-specific and constant threshold was evaluated and applied to extract lesions from a dataset of 202 T1-weighted black-blood MRI scans of subjects with up to 50% stenosis. Applied to baseline and one-year follow-up data, the method supports detailed morphology analysis over time, including volume quantification, aided by improved visualization via mesh unfolding. The extracted region was also used to analyze the signal intensity distribution within the lesion region.</p><p><strong>Results: </strong>We successfully extracted lesion regions from 297 carotid arteries, capturing a wide range of shapes with volumes ranging from 3.61 to <math><mrow><mn>996.9</mn> <mspace></mspace> <msup><mrow><mtext>mm</mtext></mrow> <mn>3</mn></msup> </mrow> </math> . The use of a constant threshold of 1.6 mm showed an intraclass correlation of 0.861 for the lesion volume and a median average surface distance of 0.594 mm in the validation set.</p><p><strong>Conclusion: </strong>The proposed method enables the extraction of lesion meshes from 3D vessel wall segmentation masks, enabling a correspondence between baseline and one-year follow-up examinations. Unfolding the lesion meshes enhances visualization, while the mesh-based analysis allows quantification of morphologic parameters and an analysis of the signal intensities in the lesion region.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"1851-1861"},"PeriodicalIF":2.3,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12476319/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144531001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating large language models on hospital health data for automated emergency triage.","authors":"Carlos Lafuente, Mehdi Rahim","doi":"10.1007/s11548-025-03475-1","DOIUrl":"10.1007/s11548-025-03475-1","url":null,"abstract":"<p><strong>Purpose: </strong>Large language models (LLMs) have a significant potential in healthcare due to their ability to process unstructured text from electronic health records (EHRs) and to generate knowledge with few or no training. In this study, we investigate the effectiveness of LLMs for clinical decision support, specifically in the context of emergency department triage, where the volume of textual data is minimal compared to other scenarios such as making a clinical diagnosis.</p><p><strong>Methods: </strong>We benchmark LLMs with traditional machine learning (ML) approaches using the Emergency Severity Index (ESI) as the gold standard criteria of triage. The benchmark includes general purpose, specialised, and fine-tuned LLMs. All models are prompted to predict ESI score from a EHRs. We use a balanced subset (n = 1000) from MIMIC-IV-ED, a large database containing records of admissions to the emergency department of Beth Israel Deaconess Medical Center.</p><p><strong>Results: </strong>Our findings show that the best-performing models have an average F1-score below 0.60. Also, while zero-shot and fine-tuned LLMs can outperform standard ML models, their performance is surpassed by ML models augmented with features derived from LLMs or knowledge graphs.</p><p><strong>Conclusion: </strong>LLMs show value for clinical decision support in scenarios with limited textual data, such as emergency department triage. The study advocates for integrating LLM knowledge representation to improve existing ML models rather than using LLMs in isolation, suggesting this as a more promising approach to enhance the accuracy of automated triage systems.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"1941-1952"},"PeriodicalIF":2.3,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144644112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Deep hashing for global registration of preoperative CT and video images for laparoscopic liver surgery.","authors":"Hanyuan Zhang, Sandun Bulathsinhala, Brian R Davidson, Matthew J Clarkson, João Ramalhinho","doi":"10.1007/s11548-025-03488-w","DOIUrl":"https://doi.org/10.1007/s11548-025-03488-w","url":null,"abstract":"","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144977831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multicenter study on the comparability of myocardial strain values acquired with different CMR scanners and analyzed with different post-processing software: insights into the \"Traveling Volunteers\" study.","authors":"Collin Goetze, Wensu Chen, Patrick Doeblin, Aylin Demir, Stephanie Wiesemann, Jochen Hansmann, Volkmar Falk, Jeanette Schulz-Menger, Jennifer Erley, Sebastian Kelle","doi":"10.1007/s11548-025-03499-7","DOIUrl":"https://doi.org/10.1007/s11548-025-03499-7","url":null,"abstract":"<p><strong>Purpose: </strong>Strain quantifies myocardial deformation. Despite its high diagnostic value, strain analyses using cardiovascular magnetic resonance (CMR) feature tracking (FT) have not been fully implemented into clinical routine due to lack of information on reproducibility. The purpose of this study was to assess the comparability of cardiovascular magnetic resonance CMR FT strain and ejection fraction (EF) measurements, obtained from different MR scanners and analyzed using different software platforms.</p><p><strong>Methods: </strong>CMR examinations were performed in 15 healthy volunteers using three different scanners (German Heart Center of the Charité, Charité Campus Berlin Buch, and Theresien-Hospital Mannheim). FT was performed using Medis Suite and Circle CVI. Inter-software/scanner agreement was determined using Bland-Altman plots, Wilcoxon test, and paired Student's t test. Intra-/inter-observer reproducibility was evaluated using intraclass correlation coefficients.</p><p><strong>Results: </strong>Left ventricular (LV) global longitudinal (GLS) and circumferential (GCS) strain did not differ between the three centers (small bias of - 1.27 to 1.32% for GLS and 0.91 to 0.69% for GCS). Inter-scanner agreement was lower regarding LV global radial strain (GRS) (bias of - 2.29 to 4.53%) and good for LV EF (bias of - 0.59 to 0.94%). Inter-software agreement was low for GLS (bias of - 5.72 to - 4.59%), GCS (- 1.13 to - 1.55%), and GRS (18.34 to 19.83%), with higher GLS/GCS and lower GRS values in CVI than Medis. LV EF showed better inter-software agreement (biases of - 0.07 to 0.06%). Intra- and inter-observer reproducibility was good for strain measurements across all scanners (bias of - 0.01 to 2.05 and 0.22 to 1.92, respectively) and software packages (ICC 0.70 to 0.90 and 0.51 to 0.89, respectively).</p><p><strong>Conclusion: </strong>Inter-scanner reproducibility for CMR FT measurements was high for GLS and GCS, suggesting potential use in routine CMR examinations. However, strain values between the two software vendors (CVI and Medis) were significantly different, indicating the need for standardization and implementation of software-specific cutoff values.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144977656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}